Learning from class-imbalanced data: Review of methods and applications
Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
humans' decision-making responses. Detecting rare events can be viewed as a prediction …
Classification of imbalanced data: review of methods and applications
Imbalance in dataset enforces numerous challenges to implement data analytic in all
existing real world applications using machine learning. Data imbalance occurs when …
existing real world applications using machine learning. Data imbalance occurs when …
A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors
J Li, Q Zhu, Q Wu, Z Fan - Information Sciences, 2021 - Elsevier
Developing techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …
presents an important challenge. Among the existing methods for addressing this problem …
Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning
Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential
solution to the multi-drug resistance problem. In particular, short-length AMPs have been …
solution to the multi-drug resistance problem. In particular, short-length AMPs have been …
Learning imbalanced datasets based on SMOTE and Gaussian distribution
T Pan, J Zhao, W Wu, J Yang - Information Sciences, 2020 - Elsevier
The learning of imbalanced datasets is a ubiquitous challenge for researchers in the fields of
data mining and machine learning. Conventional classifiers are often biased towards the …
data mining and machine learning. Conventional classifiers are often biased towards the …
AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-
resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless …
resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless …
Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis
D Gan, J Shen, B An, M Xu, N Liu - Computers & Industrial Engineering, 2020 - Elsevier
For the imbalanced classification problems, most traditional classification models only focus
on searching for an excellent classifier to maximize classification accuracy with the fixed …
on searching for an excellent classifier to maximize classification accuracy with the fixed …
Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets
One of the most popular algorithms for classification is the decision tree. However, existing
binary decision tree models do not handle well the minority class over imbalanced data sets …
binary decision tree models do not handle well the minority class over imbalanced data sets …
NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease
Coronary artery disease (CAD) is one of the main causes of cardiac death around the world.
Due to its significant impact on the society, early and accurate detection of CAD is essential …
Due to its significant impact on the society, early and accurate detection of CAD is essential …
Intrusion detection for the internet of things (IoT) based on the emperor penguin colony optimization algorithm
Abstract In the Internet of Things (IoT), the data that are sent via devices are sometimes
unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks …
unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks …